Assessment of genetic variation, heritability, and genetic advance among elite sorghum [Sorghum bicolor (L) Moench] lines for yield and yield associated traits under moisture stress areas

Assessment of genetic variation, heritability, and genetic advance among elite sorghum [Sorghum bicolor (L) Moench] lines for yield and yield associated traits under moisture stress areas | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Assessment of genetic variation, heritability, and genetic advance among elite sorghum [Sorghum bicolor (L) Moench] lines for yield and yield associated traits under moisture stress areas Werkissa Yali, Gudeta Nepir This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6178149/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract In dry lowland regions of the world, sorghum (Sorghum bicolor (L) Moench) is a significant crop extensively farmed for food, feed, fodder, and fuel. Despite extensive breeding work, sorghum production and productivity remain low in Ethiopia. To create superior genotypes in breeding programs, it is necessary to comprehend the existence and extent of genetic diversity. Therefore, this research aimed to evaluate the importance of genetic diversity, heritability, and genetic progress among the genotypes of sorghum. Using an alpha lattice design with two replications, a total of 72 genotypes were assessed during the main cropping season of 2020 in Miesso in Eastern Ethiopia and Kobo in Northern Ethiopia. For every trait, a combined analysis of variance revealed a significant difference (p<0.01) between the genotypes. Grain yield, the number of heads per plot, and the number of stands at harvest had the highest genotypic and phenotypic coefficients of variation, while the number of days to flowering, days to maturity, grain filling period, leaf length, and leaf width had the lowest. Genetic advance as a percentage of the mean (GAM) varied from 2.28% for the number of days to maturity to 97.04% for grain yield, while broad sense heritability ranged from 26.46% for panicle width to 89.67% for grain yield. The genotypes ETSC14804-4-2 (4.97 t/ha), ETSC14695-1-2 (4.7 t/ha), and ETSC14715-3-1 (4.46 t/ha) were found to be high-yielders in comparison to the others based on the current data. Still, more research is required to make better recommendations. Sorghum Traits Genotypes Heritability Genetic Variability Introduction Globally in many tropical and semi-arid regions, sorghum (Sorghum bicolor (L.) Moench) is an important cereal crop (Sanjana, 2017). According to Assefa et al. (2020) Sorghum is grown on 40 million hectares in over 105 countries in Africa, Asia, Oceania, and the Americas; 60% of this area is in Africa, where it still plays a significant role in food security. Sorghum belongs to the Gramineae family and is categorized under C4 tropical crops with chromosome number 2n = 20 (Harlan and De Wet, 1972). Naturally, it is a self-pollinating plant, and depending on the type of panicle, up to 20% of it will cross-pollinate spontaneously. It is well adapted to the range of environmental conditions in semi-arid and low land areas due to its exceptional resilience to water stress (Teshome et al., 1997). According to Doggett (1988) and Mekbib (2006), Ethiopia is the origin and diversification centers for sorghum. Sorghum is the fifth most productive crop in the world, with 57.89 million metric tons produced on 40 million hectares of harvested land (FAO, 2021). According to CSA (2020) Sorghum is grown practically in every region of Ethiopia and ranks third in terms of area coverage, after maize and tef, with a national average productivity of 2.80 tons per hectare. In dry lowlands areas, sorghum is the dominant crop, which accounts for around 66 percent of the total cultivated areas. However, research has shown that using improved varieties and production practices, there is a potential to raise sorghum productivity from 3 to 6 tons/ha (Yitayeh et al., 2019). Production of sorghum has been hampered by several factors. Gebeyehu et al. (2004) states that the main issues with sorghum production in the country's arid regions are: a dearth of drought-tolerant early maturing varieties; low soil fertility; poor stand establishment because of decreased emergence in soils that are characterized by crustiness; and insect pests like spotted stalk borers and birds. Sorghum is an important food crop and second for making injera (leavened local flat bread) after teff in Ethiopia. It is utilized for newborn food, syrup, human foods including porridge, "injera," "Kitta," and "Nifro," as well as local drinks like "Tella" and "Areke" (MoANR, 2016). According to Dial, (2012) the grain of sorghum has a high nutritive value, with 70-80% carbohydrate, 11- 13% protein, 2-5% fat, 1-3% fiber and 1-2% ash. Since the Protein in sorghum is gluten-free, it is a special meal for people who suffer from celiac disease (gluten intolerance), including diabetic patients. It also works well as a replacement for cereal grains including wheat, barley, and rye. Globally sorghum is grown for food, feed, biofuel, beer production, and silage (Pinho et al., 2015). Heritability, genetic advancement, and the type and degree of genetic variety in the base population all influence sorghum yield improvement (Jimmy et al., 2017). Breeders can choose acceptable parents for their breeding program and introduce genes from distantly related germplasm by knowing the genetic diversity of a crop. They can continue to use the genetic diversity of both cultivated crops and their wild relatives to create new and improved crop types (Tesfaye, 2018). Having a variety of genotypes available enables the production of superior cultivars that are resistant to both biotic and abiotic stressors. Further development of sorghum's genetic architecture will be made easier with an understanding of its genetic variety (Rayaprolu et al., 2017). Additionally, knowing the nature of the relationship between yield and its constituent parts aids in the concurrent selection of numerous traits linked to yield enhancements. As Ethiopia is the origin of the sorghum crop, an abundant amount of variability is present in the country. Previous researchers have shown the existence of a high degree of genetic variability in sorghum for grain yield and yield associated characters among the Ethiopian sorghum collections (Amelework et al., 2016). National and regional sorghum improvement programs have released several sorghum varieties by exploiting the existing variability for the moisture deficit in areas of Ethiopia (MoANR, 2016). Moreover, unless the genetic variability is well understood, the presence of variation in the population alone is not sufficient for improving the appropriate character. According to Atta et al. (2008) to guide future breeding policies exact estimations of heritability, phenotypic coefficients of variation, genotypic coefficients of variation, and genetic advance are basic. Therefore, the current study was conducted to quantify the genetic variability, heritability and genetic advance for yield and yield component traits among elite sorghum lines. Materials And Methods 2.1 Description of the Test Environments. The field experiment was conducted during the 2020 main cropping season at Miesso and Kobo, representing the dry lowland areas of Ethiopia. Both locations were identified due to their potential areas for production sorghum. Miesso is situated 302 kilometers from Addis Ababa, within the Oromia regional state, whereas Kobo is located in the Amhara regional state in the northern part of the country. Table 1 Description of the experimental locations. Location Altitude (masl) Rainfall (mm) Temp (◦C) Min Temp Max Latitude Longitude Soil type Mieso 1470 763 14.00 30.00 8◦30′N 39◦21′E Vertisols Kobo 1479 650 15.32 30.24 12◦09′N 39◦38′E Vertisols Source : Ethiopian Institute of Agricultural Research (EIAR), 2014. Ethiopian strategy document for sorghum. Addis Ababa, Ethiopia 2.2 Source of Experimental Materials The planting materials used for the experiment consisted of sixty-nine early maturing elite sorghum genotypes developed at the Melkassa Agricultural Research Center, along with three sorghum check varieties (Melkam, Tilahun, and Argiti). 2.3 Experimental Design and Trial Management At both locations alpha lattice design with two replications was used to conduct the trial. Each single experimental unit have two rows, each five meters in length, with a spacing of 0.75 meters between the rows and 0.20 meters between the plants. The total area of each plot was 7.5 square meters. Following the recommendations for sorghum production in the lowland areas of Ethiopia, 100 kg/ha of NPBS blended fertilizer and 50 kg/ha of urea were applied. The NPBS fertilizer was incorporated into the soil at the time of sowing, while the urea was applied as a side dressing when the plants reached knee height, approximately 35 days after emergence. Thinning was performed 3 weeks after planting to ensure adequate spacing between plants and to maintain proper plant density. All management practices were followed according to these recommended guidelines. 2.4 Data Collection Both on a plot and plant-based data were collected by random sampling by using the descriptors of sorghum (IBPGR/ICRISAT, 1993). The most important yield and yield component traits such as number of days to flowering, days to maturity, grain filling period, thousands of seed weight, plant height, total leaf area, panicle length, panicle width, grain yield, number of leaves per plant, leaf length, leaf width, overall plant aspect and stand count at harvest were recorded using standard procedures. 2.5 Data Analysis 2.5.1 Analyses of Variances (ANOVA) Before proceeding with the analysis, the data were checked for normal distribution using the Shapiro-Wilk test (Shapiro and Wilk, 1965). Analyses of variance (ANOVA) were performed using the raw data collected from 72 genotypes, utilizing R software version 4.0.3. Following the significance tests, Duncan multiple range test was used for mean separation at both 5% and 1% probability levels. In this combined analysis, the genotypes were treated as fixed factors, while the replications were considered as random factors. Analysis of variance was done using the following model:- Yijl = μ + 𝜏i + 𝛾j + ρl (j) + 𝜀ijl Where; μ is the overall (grand) mean, 𝜏i is the effect due to the ith treatment, (i=1, 2, 3…, t), γj is the effect due to the jth replication, and, (j=1, 2…, r), ρl (j) is block within replicate effect, εijl is the error term where the error terms, are independent observations from an approximately normal distribution with mean = 0 and constant variance σ² ε. Table 1: Skeleton of analysis of variance table for alpha lattice design SV DF Mean square F values Replication(r) r-1 Msr Msr/mse Blocks(rep) r(b-1) Msb Msb/mse Genotypes(g) g-1 Msg Msg/mse Error rg-rb-g+1 Mse Total rg-1 Mst Key: SV=source of variation, DF = degree of freedom, r = number of replication, b = block, g = genotypes, MS = mean squares, Msr = mean squares of replication, Msg = mean squares of genotypes, Msb = mean squares of blocks within replication, Mse = mean square of error, Mst = mean square of total. Estimation of genetic parameters Before initiating a breeding program, the presence of variation in genotypic and phenotypic traits that exist in a crop species is essential. They were estimated to observe the amount of variability among the genotypes. According to Sivasubramanian and Menon, (1973) Phenotypic coefficient of variation (PCV) and genotypic coefficient of variation (GCV) were divided as high (>20%), moderate (10-20%) and low (<10%). Using the corresponding mean square values and the formulas provided by Singh and Chaudhary (1999) and Johnson et al. (1955), genetic parameters were computed. Environmental variance (σ 2 e):s 2 e = MSe Genotypic variance (σ2g):σ 2 g= MSg-MSe /r Phenotypic variance (σ 2 p): σ 2 p = σ 2 g +σ 2 e Genotypic Coefficient of Variation (GCV) = *100 Phenotypic Coefficient of Variation (PCV) = *100 Where: PCV= Phenotypic coefficient of variation, GCV= Genotypic coefficient of variation, x = population mean of the character being evaluated. Broad sense heritability was estimated based on the formula given by Falconer and Mackay (1996) as follows: Heritability in broad sense. H 2 b = σ 2 g / σ 2 p * 100 Where: - H 2 b= Heritability in broad sense, σ 2 p= phenotypic variance, σ 2 g= genotypic Variance. Estimation Genetic advance and genetic advance as percent of means were estimated as described by Johnson et al . (1955) as: Genetic Advance (GA) = K σ p H 2 b Where: - K the standardized selection differential at 5 % (2.063), σp = the phenotypic standard Deviation and, H 2 b=heritability in broad sense. Genetic advance as percent of mean (GAM) = GA/x*100 Where: GA= genetic advance, and x = mean of population. Results and discussion 3.1 Analysis of Variance (ANOVA) According to the results of combined analysis of variance (ANOVA), revealed a highly significant difference (P < 0.001) between the 72 sorghum genotypes for fourteen of the quantitative traits. Grain yield, plant height, stand count at harvest, panicle width, panicle length, number of leaves per plant, leaf length, leaf area, number of heads per plot, days to 50% flowering, days to maturity, grain filling duration, and thousand seed weight are some of these characteristics (Table 2). According to the analysis of variance data, there is a significant degree of variation in sorghum accession for yield and its components. This variability allows breeders to select the best sorghum genotypes, as having diversity within populations is essential for a successful plant breeding program. Significant variation across sorghum genotypes for several traits was also reported by many authors. According to Adane et al. (2018), there was a highly significant difference (p<0.01) between the genotypes of sorghum in terms of plant height, head weight per plot, hundred seed weight, days to flowering, days to maturity, and grain yieldSignificant variations were also noted by Senbetay (2020) between 84 introduced sorghum accessions in terms of head weight, grain yield, plant height, days to 50% flowering and hundred seed weight. Amare et al. (2015), Endalamaw and Semahegn (2020), Gebregergs & Mekbib (2020), also notable variations in plant height, days to flowering, days to maturity, grain filling period, thousand seed weight, stay green, panicle exertion, panicle length, days to an emergency, panicle width, and grain yield. Table 2. Combined analysis of variance for 14 traits of sorghum genotypes evaluated at Miesso and Kobo in the 2020 growing season Mean square Trait Replication Block Genotype Error CV (%) (df=1) (df=16) (df=71) (df=55) Date of flowering 1 NS 2.18 NS 23.03** 2.49 1.98 Date of maturity 12.25* 2.98 NS 8.36** 1.93 1.14 Grain filling period 31.17* 5.38 NS 16.77** 6.08 5.88 Thousand seed weight 52.56* 28.85 NS 49.52** 14.06 11.10 Plant height 300.20 NS 418.90* 1852.20** 147.40 6.06 Stand at harvest 629.20 NS 34.30* 149.50** 24.80 22.65 Leaf length 5.53 NS 19.42* 60.40** 11.56 4.56 Leaf width 0.95 NS 0.35 NS 1.03* 0.56 7.95 Leaf area 5380 NS 2792 NS 7517** 2716 10.44 Panicle width 30.40 NS 2.66 NS 2.00** 0.73 9.27 Panicle length 13.04 NS 3.82 NS 11.67** 3.30 7.32 Number of heads/plot 227.51** 21.54 NS 156.24** 31.82 27.44 Number of leaves/plant 0.69** 0.21 NS 2.28** 0.33 5.79 Grain yield 0.02 NS 0.21 NS 3.15** 0.16 16.23 Key: ** = highly significant at P <0.01, * = significant at P <0.05 and NS= non-significant, respectively, CV (%) = coefficient of variation. 3.2 Estimations of Genetic Parameters 3.2.1. Estimation of Variance Components Table 3 provided estimates of the phenotypic (σ2p), genotypic (σ2g), and environmental (σ2e) variances as well as the phenotypic and genotypic coefficients of variation (PCV and GCV, respectively). There was a range of 1.88% for days to maturity and 52.54% for grain yield in the phenotypic coefficient of variation (PCV) and 1.45% for days to maturity and 49.55% for the genotypic coefficient of variation (GCV). From the analyzed sorghum genotypes traits with high value of phenotypic coefficient of variation were grain yield (52.54%), number of heads per plot (46.87) and stand count at harvest (42.73%). Also, additionally, the maximum value of GCV was obtained from grain yield (49.87%), number of heads per plot (38.71%) and stand count at harvest (35.62%). These high PCV and GCV results demonstrated that the genotypes have a wide base genetic background and significant variability to support selection-based improvement. The high GCV and PCV values for grain yield, number of stand counts at harvest, and number of heads per plot were also reported by Senbetay (2020), who assessed 84 introduced sorghum accessions. Days to maturity, days to 50% flowering, grain filling period, leaf length, and leaf width all have low PCV and GCV values. This demonstrated that those features had comparatively less variability and were more impacted by the environment for their phenotypic expression. Similarly, Endalamaw and Semahegn (2020) revealed low PCV and GCV for panicle width, flowering date, and days to maturity. Low PCV and GCV on days to maturity were also reported by Abraha et al. (2015). Out of the 14 variables that were examined, the traits of thousand seed weight, plant height, and number of leaves per plant had moderate PCV and GCV values. 3.3. Estimate of Heritability and Expected Genetic Advance The ratio of genotypic variance to overall phenotypic variance is known as broad sense heritability (Al-Tabbal et al. 2012). Selecting an appropriate breeding strategy requires evaluating the heritable and non-heritable components of overall variability. The possibility and degree of improvement through selection are shown by the heritability estimate (Robinson et al., 1956). Heritability and GVC would provide more accurate information on selection efficiency (Burton and Devane, 1953). Table 3 showed the estimated genetic advancement and broad sense heritability for 14 quantitative traits. Grain yield and panicle width had heritability values ranging from 26.46% to 89.67%. According to Robinson et al. (1956), the heritability was divided into three categories: low (0–30%), high (30–60%), and high (> 60%). Based on these categories traits such as days of flowering (80.96), plant height (79.75), stand count at harvest (68.48), leaf length (63.86), number of heads per plot (68.23), number of leaves per plant (76.72) and grain yield (89.67) shows high heritability. The qualities' high heritability indicates that their environment has less of an impact on them, making selection for them potentially simpler. In terms of days to blooming, plant height, number of leaves per plant, panicle length, and productive number of tillers per plant, Abraha et al. (2015) showed strong broad sense heritability estimates, which are consistent with this conclusion. High heritability values for plant height, leaf area, number of leaves per plant, and days to flowering were also reported by Gedifew (2020). Moderate heritability values were recorded for date of maturity (58.86), grain filling period (47.83), thousand seed weight (48.01), leaf width (32.97), leaf area (46.67) and panicle length (54.88). When estimating genetic advance, heritability becomes more useful (Johnson et al., 1955). High genetic gain (genetic progress) is not always associated with a high heritability estimate (Mulualem et al., 2018; Johnson et al., 1955). When predicting gain under selection, high heritability estimates in conjunction with a high GAM are typically more useful than heritability estimates alone (Johnson et al., 1955). According to Johnson et al. (1955), genetic advancement is measured as a percentage of the mean (GAM); low values range from 0% to 10%, moderate values range from 10% to 20%, and high values range from 20% and higher. According to Johnson et al. (1955), the current study demonstrated strong genetic advance as a percentage of the mean for plant height, stand count at harvest, number of heads per plot, and grain production. Plant height showed similar results of substantial genetic advance as a percentage of the mean (Mulualem et al., 2018). High heritability and high genetic advancement were observed in the traits under study, as evidenced by the percent of mean grain yield, stand count at harvest, and number of heads per plot. These traits are controlled by additive genetic (Panse, 1957) factors, with less environmental influence on phenotypic expression. Genetic advance may not always follow from high heritability. In this study, the estimated heritability of the total number of leaves per plant (76.72%) and the number of days until flowering (80.96%) is high, but the estimated genetic advance as a percentage of the mean is not great. Heterosis breeding may be used to take advantage of these non-additive gene activities (dominance and epistasis). A trait with additive gene action would have high heritability and genetic gain, while a trait with non-additive gene action may have high heritability but low genetic gain (Panse, 1957). Table 3. Variances and genetic parameters of sorghum traits studied at Miesso and Kobo no Trait Min Max σ2e σ2g σ2p ECV% GCV% PCV% H 2 GA GAM 1 DTF 70.5 89 2.42 10.30 12.73 1.95 4.02 4.47 80. 96 5.95 7.46 2 DTM 118 127 2.17 3.1 5.26 1.21 1.45 1.88 58. 86 2.78 2.28 3 GFP 34 47.5 6.44 5.92 5.43 11.35 5.55 8.03 47.83 3.32 7.91 4 TSW 19 45 17.39 16.06 33.46 12.35 11.87 17.13 48.01 5.72 16.94 5 PHT 144.7 283.4 208.6 821.8 1030.4 7.21 14.31 16.02 79.75 52.74 26.33 6 SH 1 38 26.92 61.29 88.21 23.61 35.62 42.73 69.48 13.44 61.16 7 LL 60.5 86.85 13.33 23.54 36.87 4.89 6.512 8.15 63.86 7.99 10.72 8 LW 8 11.35 0.51 0.26 0.77 7.61 5.376 9.36 32.97 0.59 6.36 9 LA 389.3 657.8 2732.98 2391.8 5124.7 10.47 9.79 14.34 46.67 68.83 13.79 10 PW 6.65 11.7 1.16 0.42 1.58 11.72 7.04 13.68 26.46 0.69 7.46 11 PL 19.85 32.33 3.42 4.13 7.55 7.45 8.19 11.06 54.88 3.11 12.51 12 NHP 1 35 29.51 63.37 92.87 26.42 38.71 46.87 68.23 13.55 65.87 13 NLPP 7 12 0.30 0.99 1.29 5.55 10.07 11.49 76.72 1.79 18.17 14 GY 0.29 4.97 0.17 1.48 1.66 16.88 49.75 52.54 89.67 2.38 97.04 Key: DTF=date of flowering, DTM= date of maturity, GFP=grain filling period, TSW=thousand seed weight, PHT=plant height, SH=stand at harvest, LL=leaf length, LW=leaf width, LA=leaf area, PW=panicle width, PL=panicle length, NHP=number of head per plot, NLPP=number of leaf per plant, GY= grain yield, GA= genetic advance, GAM= genetic advance as mean,H 2 =broad sense heritability, GCV=genotypic coefficient of variation, PCV= phenotypic coefficient of variation, ECV=environmental coefficient variation Summary and Conclusions Understanding the relationship and amount of genetic variability between yield and other agronomic characteristics is crucial in plant breeding programs, as it forms the foundation for effective selection. In study 72, sorghum elite genotypes were evaluated for their drought tolerance under stress conditions during the season of 2020 at the Miesso and Kobo site. Alpha lattice design with two replications was used to employ the experimental design. The analysis of variance showed significant differences among the genotypes for all the traits examined. Given that successful crop improvement requires heterogeneity within populations, the presence of significant genotype variability suggests the possibility of enhancing these qualities through both direct and indirect selection. According to the present results of combined analysis from both locations, higher phenotypic over genotypic coefficient of variation were recorded for all traits with a range of GCV 1.45% for number of days to maturity to 49.75 % for grain yield, PCV 1.88% for number of days to maturity to 52.54% for grain yield. Broad sense heritability ranges from 32.97% for leaf width to 89.67% for grain yield, with genetic progress as a percentage of the mean ranging from 2.28% for days to maturity to 97.04% for grain output. Grain yield, number of heads per plot, and stand count at harvest showed higher GCV along with higher heritability and GAM and thus such traits are controlled by additive gene action and could be improved through recurrent selection. However, the number of leaves per plant, leaf length, and days to 50% flowering had lower GAM and higher heritability, indicating that these traits are mostly governed by non-additive gene types and cannot be changed through selection. The current data indicated that the genotypes ETSC14804-4-2 (4.97 t/ha), ETSC14695-1-2 (4.7 t/ha), and ETSC14715-3-1 (4.46 t/ha) were high yielders compared to the other genotypes under investigation. However, it is important to note that these results and conclusions are derived from data collected during a one-year field investigation at both locations. Thus, to get thorough results and draw reliable conclusions and suggestions, a greater number of accessions will be evaluated under moisture stress areas. Declarations Acknowledgments We would like to express our sincere gratitude to the Ethiopian Institute of Agricultural Research for their financial support throughout the study. Additionally, we extend our heartfelt thanks to the staff of the Melkassa Agricultural Research Center (National Sorghum Program) and the Chiro National Sorghum Research and Training Centers for their generosity in allowing us to use their facilities and for their support during the field experiments. Conflict of interest The authors declare that there are no conflicts of interest. References Abraha, T., Githiri, S. M., Kasili, R., Araia, W. and Nyende, A. B. (2015). 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Correlation and Path Coefficient Analysis for Agronomical Traits of Lowland Adapted Ethiopian Sorghum Genotypes [ Sorghum bicolor (L.) Moench ] Genotypes. Greener journal of Agricultural Sciences, 8:155-159. Mutegi et al. (2011). Genetic structure and relationship within and between cultivated and wild sorghum ( Sorghum bicolor (L.) Moench ) in Kenya as revealed by microsatellite markers. Theoretical and Applied Genetics, 122(5), 989–1004. Panse, V.G., (1957). Genetics of quantitative characters about plant breeding. Indian Journal of Genetics and Plant Breeding. 17:318–328. Pinho, R. M. A., Santos, E. M., Oliveira, J. S. D., Bezerra, H. F. C., Freitas, P. M. D. D., Perazzo, A. F., ... & Silva, A. P. G. D. (2015). Sorghum cultivars of different purposes, for silage. Ciência Rural , 45 , 298-303. Rayaprolu, L., Ashok Kumar, A., Manohar Rao, D., & Deshpande, S. P. (2017). Genetic Variability for Agronomic Traits in Sorghum Minicore Collection. International Journal of Agriculture Innovations and Research , 6 (3), 533-537. Robinson H.F., Hamson G.H. and Comstock R.E. (1956). Biometrical studies of yield in segregating populations of Korean Lespedeza. Agronomy Journal 40: 260-672. Sanjana Reddy, P. (2017). Sorghum, sorghum bicolor (L.) Moench. Millets and Sorghum: Biology and Genetic Improvement , 1-48. Senbetay, T. (2020) ‘Genetic Variability, Heritability, Genetic Advance and Trait Associations in Selected Sorghum ( Sorghum bicolor L. Moench ) Accessions in Ethiopia’, Journal of Biology, Agriculture and Healthcare , 10(12),1–8. Shapiro, S. S., & Wilk, M. B. (1965). An analysis of variance test for normality (complete samples). Biometrika , 52 (3/4), 591-611. Sharma, J. R. (2006). Statistical and biometrical techniques in plant breeding . New Age International. Singh R.K. and Chaudhary B.D. (1999). Biometrical Methods in Quantitative Genetics Analysis. Kalyani Publishers. New Delhi, India. P318. Sivasubramanian, S. and Menon, M. (1973). Heterosis and inbreeding depression in rice. MadrasAgric. J, 60 , 1139-1140. Tesfaye, K. (2018) ‘Genetic diversity study of sorghum ( Sorghum bicolor (L.) Moench ) genotypes, Ethiopia’, Acta Universitatis Sapientiae, Agriculture and Environment , 9(1), 44–54. Teshome, A., Baum, B. R., Fahrig, L., Torrance, J. K., Arnason, T. J., Lambert, J. D. (1997), Sorghum [ Sorghum bicolor (L.) Moench] landrace variation and classification in North Shewa and South Welo, Ethiopia. Euphytica 97, 255–263. Yitayeh, Z. S., Mindaye, T. T., & Bisetegn, K. B. (2019). AMMI and GGE Analysis of G× E and Yield Stability of Early Maturing Sorghum [ Sorghum bicolor (L.) Moench ] Genotypes in Dry Lowland Areas of Ethiopia. Advances in Crop Science and Technology , 5 , 425. Additional Declarations No competing interests reported. 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Moench) is an important cereal crop (Sanjana, 2017). According to Assefa et al. (2020) Sorghum is grown on 40 million hectares in over 105 countries in Africa, Asia, Oceania, and the Americas; 60% of this area is in Africa, where it still plays a significant role in food security. Sorghum belongs to the Gramineae family and is categorized under C4 tropical crops with chromosome number 2n = 20 (Harlan and De Wet, 1972). Naturally, it is a self-pollinating plant, and depending on the type of panicle, up to 20% of it will cross-pollinate spontaneously. \u0026nbsp;It is well adapted to the range of environmental conditions in semi-arid and low land areas due to its exceptional resilience to water stress (Teshome et al., 1997). According to Doggett (1988) and Mekbib (2006), Ethiopia is the origin and diversification centers for sorghum.\u003c/p\u003e\n\u003cp\u003eSorghum is the fifth most productive crop in the world, with 57.89 million metric tons produced on 40 million hectares of harvested land (FAO, 2021). According to CSA (2020) Sorghum is grown practically in every region of Ethiopia and ranks third in terms of area coverage, after maize and tef, with a national average productivity of 2.80 tons per hectare. In dry lowlands areas, sorghum is the dominant crop, which accounts for around 66 percent of the total cultivated areas. However, research has shown that using improved varieties and production practices, there is a potential to raise sorghum productivity from 3 to 6 tons/ha (Yitayeh et al., 2019). Production of sorghum has been hampered by several factors. \u0026nbsp;Gebeyehu et al. (2004) states that the main issues with sorghum production in the country\u0026apos;s arid regions are: a dearth of drought-tolerant early maturing varieties; low soil fertility; poor stand establishment because of decreased emergence in soils that are characterized by crustiness; and insect pests like spotted stalk borers and birds.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eSorghum is an important food crop and second for making injera (leavened local flat bread) after teff in Ethiopia. It is utilized for newborn food, syrup, human foods including porridge, \u0026quot;injera,\u0026quot; \u0026quot;Kitta,\u0026quot; and \u0026quot;Nifro,\u0026quot; as well as local drinks like \u0026quot;Tella\u0026quot; and \u0026quot;Areke\u0026quot; (MoANR, 2016). According to Dial, (2012) the grain of sorghum has a high nutritive value, with 70-80% carbohydrate, 11- 13% protein, 2-5% fat, 1-3% fiber and 1-2% ash. Since the\u0026nbsp;Protein in sorghum is gluten-free, it is a special meal for people who suffer from celiac disease (gluten intolerance), including diabetic patients. It also works well as a replacement for cereal grains including wheat, barley, and rye.\u0026nbsp;Globally sorghum is grown for food, feed, biofuel, beer production, and silage (Pinho et al., 2015).\u003c/p\u003e\n\u003cp\u003eHeritability, genetic advancement, and the type and degree of genetic variety in the base population all influence sorghum yield improvement (Jimmy et al., 2017). Breeders can choose acceptable parents for their breeding program and introduce genes from distantly related germplasm by knowing the genetic diversity of a crop. They can continue to use the genetic diversity of both cultivated crops and their wild relatives to create new and improved crop types (Tesfaye, 2018). Having a variety of genotypes available enables the production of superior cultivars that are resistant to both biotic and abiotic stressors. Further development of sorghum\u0026apos;s genetic architecture will be made easier with an understanding of its genetic variety (Rayaprolu et al., 2017). Additionally, knowing the nature of the relationship between yield and its constituent parts aids in the concurrent selection of numerous traits linked to yield enhancements.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAs Ethiopia is the origin of the sorghum crop, an abundant amount of variability is present in the country. Previous researchers have shown the existence of a high degree of genetic variability in sorghum for grain yield and yield associated characters among the Ethiopian sorghum collections (Amelework et al., 2016). \u0026nbsp;National and regional sorghum improvement programs have released several sorghum varieties by exploiting the existing variability for the moisture deficit in areas of Ethiopia (MoANR, 2016). Moreover, unless the genetic variability is well understood, the presence of variation in the population alone is not sufficient for improving the appropriate character. According to Atta et al. (2008) to guide future breeding policies exact estimations of heritability, phenotypic coefficients of variation, genotypic coefficients of variation, and genetic advance are basic. Therefore, the current study was conducted to quantify the genetic variability, heritability and genetic advance for yield and yield component traits among elite sorghum lines.\u003c/p\u003e"},{"header":"Materials And Methods","content":"\u003ch2\u003e2.1 Description of the Test Environments.\u003c/h2\u003e\n\u003cp\u003e\u003cspan id=\"_Toc53829109\"\u003eThe field experiment was conducted during the 2020 main cropping season at Miesso and Kobo, representing the dry lowland areas of Ethiopia. Both locations were identified due to their potential areas for production sorghum. Miesso is situated 302 kilometers from Addis Ababa, within the Oromia regional state, whereas Kobo is located in the Amhara regional state in the northern part of the country.\u0026nbsp;\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 1\u0026nbsp;\u003c/strong\u003eDescription of the experimental locations.\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003eLocation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003eAltitude (masl)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003eRainfall (mm)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003eTemp (◦C) Min\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003eTemp Max\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003eLatitude\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003eLongitude\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003eSoil type\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003eMieso\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e1470\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e763\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e14.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e30.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e8◦30\u0026prime;N\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e39◦21\u0026prime;E\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003eVertisols\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003eKobo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e1479\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e650\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e15.32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e30.24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e12◦09\u0026prime;N\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e39◦38\u0026prime;E\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003eVertisols\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003eSource\u003c/strong\u003e: Ethiopian Institute of Agricultural Research (EIAR), 2014. Ethiopian strategy document for sorghum. Addis Ababa, Ethiopia\u003c/p\u003e\n\u003ch2\u003e\u003cspan id=\"_Toc76034143\"\u003e2.2 Source of Experimental Materials\u003c/span\u003e\u003c/h2\u003e\n\u003cp\u003eThe planting materials used for the experiment consisted of sixty-nine early maturing elite sorghum genotypes developed at the Melkassa Agricultural Research Center, along with three sorghum check varieties (Melkam, Tilahun, and Argiti). \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n\u003ch2 id=\"_Toc76034144\"\u003e2.3 Experimental Design and Trial Management\u003c/h2\u003e\n\u003cp\u003eAt both locations alpha lattice design with two replications was used to conduct the trial. Each single experimental unit have two rows, each five meters in length, with a spacing of 0.75 meters between the rows and 0.20 meters between the plants. The total area of each plot was 7.5 square meters. Following the recommendations for sorghum production in the lowland areas of Ethiopia, 100 kg/ha of NPBS blended fertilizer and 50 kg/ha of urea were applied. The NPBS fertilizer was incorporated into the soil at the time of sowing, while the urea was applied as a side dressing when the plants reached knee height, approximately 35 days after emergence. Thinning was performed 3 weeks after planting to ensure adequate spacing between plants and to maintain proper plant density. All management practices were followed according to these recommended guidelines.\u003c/p\u003e\n\u003ch2 id=\"_Toc53829110\"\u003e2.4 Data Collection\u003c/h2\u003e\n\u003cp\u003eBoth on a plot and plant-based data were collected by random sampling by using the descriptors of sorghum (IBPGR/ICRISAT, 1993). \u0026nbsp;The most important yield and yield component traits such as number of days to flowering, days to maturity, grain filling period, thousands of seed weight, plant height, total leaf area, panicle length, panicle width, \u0026nbsp; grain yield, number of leaves per plant, leaf length, \u0026nbsp;leaf width, overall plant aspect and stand count at harvest were recorded using standard procedures.\u003c/p\u003e\n\u003ch2 id=\"_Toc76034146\"\u003e2.5 Data Analysis\u003c/h2\u003e\n\u003cp\u003e\u003cstrong\u003e2.5.1 Analyses of Variances (ANOVA)\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eBefore proceeding with the analysis, the data were checked for normal distribution using the Shapiro-Wilk test (Shapiro and Wilk, 1965). Analyses of variance (ANOVA) were performed using the raw data collected from 72 genotypes, utilizing R software version 4.0.3. Following the significance tests, Duncan multiple range test was used for mean separation at both 5% and 1% probability levels. In this combined analysis, the genotypes were treated as fixed factors, while the replications were considered as random factors.\u003c/p\u003e\n\u003cp\u003eAnalysis of variance was done using the following model:-\u003c/p\u003e\n\u003cp\u003eYijl = \u0026mu; + 𝜏i + 𝛾j + \u0026rho;l (j) + 𝜀ijl\u003c/p\u003e\n\u003cp\u003eWhere; \u0026mu; is the overall (grand) mean, 𝜏i is the effect due to the ith treatment, (i=1, 2, 3\u0026hellip;, t),\u003cbr\u003e\u0026gamma;j is the effect due to the jth replication, and, (j=1, 2\u0026hellip;, r), \u0026rho;l (j) is block within replicate\u003cbr\u003eeffect, \u0026epsilon;ijl is the error term where the error terms, are independent observations from an\u003cbr\u003eapproximately normal distribution with mean = 0 and constant variance \u0026sigma;\u0026sup2; \u0026epsilon;.\u003c/p\u003e\n\u003cp id=\"_Toc53829129\"\u003eTable 1: Skeleton of analysis of variance table for alpha lattice design\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"607\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp; SV\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eDF\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMean square\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 276px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eF values\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\n \u003cp\u003eReplication(r)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003er-1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003eMsr\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 276px;\"\u003e\n \u003cp\u003eMsr/mse\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\n \u003cp\u003eBlocks(rep)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003er(b-1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003eMsb\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 276px;\"\u003e\n \u003cp\u003eMsb/mse\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\n \u003cp\u003eGenotypes(g)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003eg-1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003eMsg\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 276px;\"\u003e\n \u003cp\u003eMsg/mse\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\n \u003cp\u003eError\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003erg-rb-g+1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003eMse\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 276px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\n \u003cp\u003eTotal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003erg-1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003eMst\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 276px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cem\u003eKey: SV=source of variation, DF = degree of freedom, r = number of replication, b = block, g = genotypes, MS = mean squares, Msr = mean squares of\u003c/em\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003cem\u003ereplication, Msg = mean squares of genotypes, Msb = mean squares of blocks within\u003c/em\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003cem\u003ereplication, Mse =\u003c/em\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003cem\u003emean square of error, Mst = mean square of total.\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEstimation of genetic parameters\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eBefore initiating a breeding program, the presence of variation in genotypic and phenotypic traits that exist in a crop species is essential. They were estimated to observe the amount of variability among the genotypes. According to Sivasubramanian and Menon, (1973) Phenotypic coefficient of variation (PCV) and genotypic coefficient of variation (GCV) were divided as high (\u0026gt;20%), moderate (10-20%) and low (\u0026lt;10%). Using the corresponding mean square values and the formulas provided by Singh and Chaudhary (1999) and Johnson et al. (1955), genetic parameters were computed.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eEnvironmental variance (\u0026sigma;\u003csup\u003e2\u003c/sup\u003ee):s\u003csup\u003e2\u003c/sup\u003e\u003cem\u003ee\u003c/em\u003e=\u003cem\u003eMSe\u003cbr\u003e\u003c/em\u003eGenotypic variance (\u0026sigma;2g):\u0026sigma;\u003csup\u003e2\u003c/sup\u003eg=\u003cem\u003e\u003cu\u003eMSg-MSe\u003c/u\u003e/r\u003cbr\u003e\u0026nbsp;\u003c/em\u003ePhenotypic variance (\u0026sigma;\u003csup\u003e2\u003c/sup\u003ep): \u0026sigma;\u003csup\u003e2\u003c/sup\u003ep = \u0026sigma;\u003csup\u003e2\u003c/sup\u003eg +\u0026sigma;\u003csup\u003e2\u003c/sup\u003ee\u003c/p\u003e\n\u003cp\u003eGenotypic Coefficient of Variation (GCV) =\u0026nbsp;\u003cimg width=\"48\" height=\"25\" src=\"data:image/png;base64,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\" alt=\"image\"\u003e*100\u003cbr\u003e Phenotypic Coefficient of Variation (PCV) = \u003cimg width=\"51\" height=\"25\" src=\"data:image/png;base64,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\" alt=\"image\"\u003e*100\u003c/p\u003e\n\u003cp\u003eWhere: PCV= Phenotypic coefficient of variation, GCV= Genotypic coefficient of variation,\u003cbr\u003ex \u003cstrong\u003e=\u0026nbsp;\u003c/strong\u003epopulation mean of the character being evaluated.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eBroad sense heritability was estimated based on the formula given by Falconer and Mackay (1996) as follows: Heritability in broad sense.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eH\u003csup\u003e2\u003c/sup\u003eb = \u0026sigma;\u003csup\u003e2\u003c/sup\u003eg / \u0026sigma;\u003csup\u003e2\u003c/sup\u003ep * 100\u003c/p\u003e\n\u003cp\u003eWhere: - H\u003csup\u003e2\u003c/sup\u003eb= Heritability in broad sense, \u0026sigma;\u003csup\u003e2\u003c/sup\u003ep= phenotypic variance, \u0026sigma;\u003csup\u003e2\u003c/sup\u003eg= genotypic\u003cbr\u003e\u0026nbsp;Variance.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eEstimation Genetic advance and genetic advance as percent of means were estimated as described by Johnson \u003cem\u003eet al\u003c/em\u003e. (1955) as:\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eGenetic Advance (GA) = K \u0026sigma; p H\u003csup\u003e2\u003c/sup\u003eb\u003c/p\u003e\n\u003cp\u003eWhere: - K the standardized selection differential at 5 % (2.063), \u0026sigma;p = the phenotypic standard\u003cbr\u003eDeviation and, H\u003csup\u003e2\u003c/sup\u003eb=heritability in broad sense. Genetic advance as percent of mean (GAM) = GA/x*100 Where: GA= genetic advance, and x \u003cstrong\u003e=\u0026nbsp;\u003c/strong\u003emean of population.\u0026nbsp;\u003c/p\u003e"},{"header":"Results and discussion","content":"\u003ch2\u003e3.1 Analysis of Variance (ANOVA)\u003c/h2\u003e\n\u003cp\u003eAccording to the results of combined analysis of variance (ANOVA), revealed a highly significant difference (P \u0026lt; 0.001) between the 72 sorghum genotypes for fourteen of the quantitative traits. Grain yield, plant height, stand count at harvest, panicle width, panicle length, number of leaves per plant, leaf length, leaf area, number of heads per plot, days to 50% flowering, days to maturity, grain filling duration, and thousand seed weight are some of these characteristics (Table 2). According to the analysis of variance data, there is a significant degree of variation in sorghum accession for yield and its components. This variability allows breeders to select the best sorghum genotypes, as having diversity within populations is essential for a successful plant breeding program.\u003c/p\u003e\n\u003cp\u003eSignificant variation across sorghum genotypes for several traits was also reported by many authors. According to Adane et al. (2018), there was a highly significant difference (p\u0026lt;0.01) between the genotypes of sorghum in terms of plant height, head weight per plot, hundred seed weight, days to flowering, days to maturity, and grain yieldSignificant variations were also noted by Senbetay (2020) between 84 introduced sorghum accessions in terms of head weight, grain yield, plant height, days to 50% flowering and hundred seed weight.\u003c/p\u003e\n\u003cp\u003eAmare et al. (2015), Endalamaw and Semahegn (2020), Gebregergs \u0026amp; Mekbib (2020), also notable variations in plant height, days to flowering, days to maturity, grain filling period, thousand seed weight, stay green, panicle exertion, panicle length, days to an emergency, panicle width, and grain yield.\u003c/p\u003e\n\u003cp id=\"_Toc70518198\"\u003eTable 2. Combined analysis of variance for 14 traits of sorghum genotypes evaluated at Miesso and Kobo in the 2020 growing season\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"737\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 21.3052%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 24.1843%;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Mean square\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 13.0518%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14.3954%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 11.9002%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.1631%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 21.3052%;\"\u003e\n \u003cp\u003eTrait\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 24.1843%;\"\u003e\n \u003cp\u003eReplication\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.0518%;\"\u003e\n \u003cp\u003eBlock\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.3954%;\"\u003e\n \u003cp\u003eGenotype\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.9002%;\"\u003e\n \u003cp\u003eError\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.1631%;\"\u003e\n \u003cp\u003eCV (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 21.3052%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 24.1843%;\"\u003e\n \u003cp\u003e(df=1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.0518%;\"\u003e\n \u003cp\u003e(df=16)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.3954%;\"\u003e\n \u003cp\u003e(df=71)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.9002%;\"\u003e\n \u003cp\u003e(df=55)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 15.1631%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 21.3052%;\"\u003e\n \u003cp\u003eDate of flowering\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 24.1843%;\"\u003e\n \u003cp\u003e1\u003csup\u003e\u0026nbsp;NS\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.0518%;\"\u003e\n \u003cp\u003e2.18\u003csup\u003e\u0026nbsp;NS\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.3954%;\"\u003e\n \u003cp\u003e23.03**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.9002%;\"\u003e\n \u003cp\u003e2.49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.1631%;\"\u003e\n \u003cp\u003e1.98\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 21.3052%;\"\u003e\n \u003cp\u003eDate of maturity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 24.1843%;\"\u003e\n \u003cp\u003e12.25*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.0518%;\"\u003e\n \u003cp\u003e2.98\u003csup\u003eNS\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.3954%;\"\u003e\n \u003cp\u003e8.36**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.9002%;\"\u003e\n \u003cp\u003e1.93\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.1631%;\"\u003e\n \u003cp\u003e1.14\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 21.3052%;\"\u003e\n \u003cp\u003eGrain filling period\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 24.1843%;\"\u003e\n \u003cp\u003e31.17*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.0518%;\"\u003e\n \u003cp\u003e5.38\u003csup\u003e\u0026nbsp;NS\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.3954%;\"\u003e\n \u003cp\u003e16.77**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.9002%;\"\u003e\n \u003cp\u003e6.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.1631%;\"\u003e\n \u003cp\u003e5.88\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 21.3052%;\"\u003e\n \u003cp\u003eThousand seed weight\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 24.1843%;\"\u003e\n \u003cp\u003e52.56*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.0518%;\"\u003e\n \u003cp\u003e28.85\u003csup\u003e\u0026nbsp;NS\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.3954%;\"\u003e\n \u003cp\u003e49.52**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.9002%;\"\u003e\n \u003cp\u003e14.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.1631%;\"\u003e\n \u003cp\u003e11.10\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 21.3052%;\"\u003e\n \u003cp\u003ePlant height\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 24.1843%;\"\u003e\n \u003cp\u003e300.20\u003csup\u003e\u0026nbsp;NS\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.0518%;\"\u003e\n \u003cp\u003e418.90*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.3954%;\"\u003e\n \u003cp\u003e1852.20**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.9002%;\"\u003e\n \u003cp\u003e147.40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.1631%;\"\u003e\n \u003cp\u003e6.06\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 21.3052%;\"\u003e\n \u003cp\u003eStand at harvest\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 24.1843%;\"\u003e\n \u003cp\u003e629.20\u003csup\u003e\u0026nbsp;NS\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.0518%;\"\u003e\n \u003cp\u003e34.30*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.3954%;\"\u003e\n \u003cp\u003e149.50**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.9002%;\"\u003e\n \u003cp\u003e24.80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.1631%;\"\u003e\n \u003cp\u003e22.65\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 21.3052%;\"\u003e\n \u003cp\u003eLeaf length\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 24.1843%;\"\u003e\n \u003cp\u003e5.53\u003csup\u003e\u0026nbsp;NS\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.0518%;\"\u003e\n \u003cp\u003e19.42*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.3954%;\"\u003e\n \u003cp\u003e60.40**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.9002%;\"\u003e\n \u003cp\u003e11.56\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.1631%;\"\u003e\n \u003cp\u003e4.56\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 21.3052%;\"\u003e\n \u003cp\u003eLeaf width\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 24.1843%;\"\u003e\n \u003cp\u003e0.95\u003csup\u003e\u0026nbsp;NS\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.0518%;\"\u003e\n \u003cp\u003e0.35\u003csup\u003e\u0026nbsp;NS\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.3954%;\"\u003e\n \u003cp\u003e1.03*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.9002%;\"\u003e\n \u003cp\u003e0.56\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.1631%;\"\u003e\n \u003cp\u003e7.95\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 21.3052%;\"\u003e\n \u003cp\u003eLeaf area\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 24.1843%;\"\u003e\n \u003cp\u003e5380\u003csup\u003e\u0026nbsp;NS\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.0518%;\"\u003e\n \u003cp\u003e2792\u003csup\u003e\u0026nbsp;NS\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.3954%;\"\u003e\n \u003cp\u003e7517**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.9002%;\"\u003e\n \u003cp\u003e2716\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.1631%;\"\u003e\n \u003cp\u003e10.44\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 21.3052%;\"\u003e\n \u003cp\u003ePanicle width\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 24.1843%;\"\u003e\n \u003cp\u003e30.40\u003csup\u003e\u0026nbsp;NS\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.0518%;\"\u003e\n \u003cp\u003e2.66\u003csup\u003e\u0026nbsp;NS\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.3954%;\"\u003e\n \u003cp\u003e2.00**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.9002%;\"\u003e\n \u003cp\u003e0.73\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.1631%;\"\u003e\n \u003cp\u003e9.27\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 21.3052%;\"\u003e\n \u003cp\u003ePanicle length\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 24.1843%;\"\u003e\n \u003cp\u003e13.04\u003csup\u003e\u0026nbsp;NS\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.0518%;\"\u003e\n \u003cp\u003e3.82\u003csup\u003e\u0026nbsp;NS\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.3954%;\"\u003e\n \u003cp\u003e11.67**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.9002%;\"\u003e\n \u003cp\u003e3.30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.1631%;\"\u003e\n \u003cp\u003e7.32\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 21.3052%;\"\u003e\n \u003cp\u003eNumber of heads/plot\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 24.1843%;\"\u003e\n \u003cp\u003e227.51**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.0518%;\"\u003e\n \u003cp\u003e21.54\u003csup\u003e\u0026nbsp;NS\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.3954%;\"\u003e\n \u003cp\u003e156.24**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.9002%;\"\u003e\n \u003cp\u003e31.82\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.1631%;\"\u003e\n \u003cp\u003e27.44\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 21.3052%;\"\u003e\n \u003cp\u003eNumber of leaves/plant\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 24.1843%;\"\u003e\n \u003cp\u003e0.69**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.0518%;\"\u003e\n \u003cp\u003e0.21\u003csup\u003e\u0026nbsp;NS\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.3954%;\"\u003e\n \u003cp\u003e2.28**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.9002%;\"\u003e\n \u003cp\u003e0.33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.1631%;\"\u003e\n \u003cp\u003e5.79\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 21.3052%;\"\u003e\n \u003cp\u003eGrain yield\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 24.1843%;\"\u003e\n \u003cp\u003e0.02\u003csup\u003e\u0026nbsp;NS\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.0518%;\"\u003e\n \u003cp\u003e0.21\u003csup\u003e\u0026nbsp;NS\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.3954%;\"\u003e\n \u003cp\u003e3.15**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.9002%;\"\u003e\n \u003cp\u003e0.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.1631%;\"\u003e\n \u003cp\u003e16.23\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003eKey:\u003c/strong\u003e \u003cstrong\u003e\u003cem\u003e**\u003c/em\u003e\u003c/strong\u003e\u003cem\u003e= highly significant at P \u0026lt;0.01, \u003cstrong\u003e*\u0026nbsp;\u003c/strong\u003e= significant at P \u0026lt;0.05 and NS= non-significant, respectively, CV (%) = coefficient of variation.\u003c/em\u003e\u003c/p\u003e\n\u003ch2 id=\"_Toc76034150\"\u003e3.2 Estimations of Genetic Parameters\u003c/h2\u003e\n\u003ch3\u003e\u003cspan id=\"_Toc76034151\"\u003e3.2.1. Estimation of Variance Components\u003c/span\u003e\u003c/h3\u003e\n\u003cp\u003eTable 3 provided estimates of the phenotypic (\u0026sigma;2p), genotypic (\u0026sigma;2g), and environmental (\u0026sigma;2e) variances as well as the phenotypic and genotypic coefficients of variation (PCV and GCV, respectively). There was a range of 1.88% for days to maturity and 52.54% for grain yield in the phenotypic coefficient of variation (PCV) and 1.45% for days to maturity and 49.55% for the genotypic coefficient of variation (GCV). \u0026nbsp;From the analyzed sorghum genotypes traits with high value of phenotypic coefficient of variation were grain yield (52.54%), number of heads per plot (46.87) and stand count at harvest (42.73%). Also, additionally, the maximum value of GCV was obtained from grain yield (49.87%), number of heads per plot (38.71%) and stand count at harvest (35.62%). These high PCV and GCV results demonstrated that the genotypes have a wide base genetic background and significant variability to support selection-based improvement. The high GCV and PCV values for grain yield, number of stand counts at harvest, and number of heads per plot were also reported by Senbetay (2020), who assessed 84 introduced sorghum accessions.\u003c/p\u003e\n\u003cp\u003eDays to maturity, days to 50% flowering, grain filling period, leaf length, and leaf width all have low PCV and GCV values. This demonstrated that those features had comparatively less variability and were more impacted by the environment for their phenotypic expression. Similarly, Endalamaw and Semahegn (2020) revealed low PCV and GCV for panicle width, flowering date, and days to maturity. Low PCV and GCV on days to maturity were also reported by Abraha et al. (2015). Out of the 14 variables that were examined, the traits of thousand seed weight, plant height, and number of leaves per plant had moderate PCV and GCV values.\u003c/p\u003e\n\u003ch2 id=\"_Toc76034152\"\u003e3.3. Estimate of Heritability and Expected Genetic Advance\u003c/h2\u003e\n\u003cp\u003eThe ratio of genotypic variance to overall phenotypic variance is known as broad sense heritability (Al-Tabbal et al. 2012). Selecting an appropriate breeding strategy requires evaluating the heritable and non-heritable components of overall variability. The possibility and degree of improvement through selection are shown by the heritability estimate (Robinson et al., 1956). Heritability and GVC would provide more accurate information on selection efficiency (Burton and Devane, 1953).\u003c/p\u003e\n\u003cp\u003eTable 3 showed the estimated genetic advancement and broad sense heritability for 14 quantitative traits. Grain yield and panicle width had heritability values ranging from 26.46% to 89.67%. According to Robinson et al. (1956), the heritability was divided into three categories: low (0\u0026ndash;30%), high (30\u0026ndash;60%), and high (\u0026gt; 60%). Based on these categories traits such as days of flowering (80.96), plant height (79.75), stand count at harvest (68.48), leaf length (63.86), number of heads per plot (68.23), number of leaves per plant (76.72) and grain yield (89.67) shows high heritability. The qualities\u0026apos; high heritability indicates that their environment has less of an impact on them, making selection for them potentially simpler. In terms of days to blooming, plant height, number of leaves per plant, panicle length, and productive number of tillers per plant, Abraha et al. (2015) showed strong broad sense heritability estimates, which are consistent with this conclusion. High heritability values for plant height, leaf area, number of leaves per plant, and days to flowering were also reported by Gedifew (2020). Moderate heritability values were recorded for date of maturity (58.86), grain filling period (47.83), thousand seed weight (48.01), leaf width (32.97), leaf area (46.67) and panicle length (54.88).\u003c/p\u003e\n\u003cp\u003eWhen estimating genetic advance, heritability becomes more useful (Johnson et al., 1955).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eHigh genetic gain (genetic progress) is not always associated with a high heritability estimate (Mulualem et al., 2018; Johnson et al., 1955). When predicting gain under selection, high heritability estimates in conjunction with a high GAM are typically more useful than heritability estimates alone (Johnson et al., 1955). According to Johnson et al. (1955), genetic advancement is measured as a percentage of the mean (GAM); low values range from 0% to 10%, moderate values range from 10% to 20%, and high values range from 20% and higher.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAccording to Johnson et al. (1955), the current study demonstrated strong genetic advance as a percentage of the mean for plant height, stand count at harvest, number of heads per plot, and grain production. Plant height showed similar results of substantial genetic advance as a percentage of the mean (Mulualem et al., 2018). High heritability and high genetic advancement were observed in the traits under study, as evidenced by the percent of mean grain yield, stand count at harvest, and number of heads per plot. These traits are controlled by additive genetic (Panse, 1957) factors, with less environmental influence on phenotypic expression.\u003c/p\u003e\n\u003cp\u003eGenetic advance may not always follow from high heritability. In this study, the estimated heritability of the total number of leaves per plant (76.72%) and the number of days until flowering (80.96%) is high, but the estimated genetic advance as a percentage of the mean is not great. Heterosis breeding may be used to take advantage of these non-additive gene activities (dominance and epistasis). A trait with additive gene action would have high heritability and genetic gain, while a trait with non-additive gene action may have high heritability but low genetic gain (Panse, 1957).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTable 3. Variances and genetic parameters of sorghum traits studied at Miesso and Kobo\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"847\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 3.2872%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eno\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 7.7855%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTrait\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 6.5744%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMin\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 6.7474%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMax\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 9.6886%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026sigma;2e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 8.3045%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026sigma;2g\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 8.4775%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026sigma;2p\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 9.3426%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eECV%\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 9.6886%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eGCV%\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 9.1696%;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePCV%\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 6.7474%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eH\u003csup\u003e2\u003c/sup\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 6.7474%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eGA\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 7.4394%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eGAM\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 3.2872%;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 7.7855%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eDTF\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 6.5744%;\"\u003e\n \u003cp\u003e70.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 6.7474%;\"\u003e\n \u003cp\u003e89\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 9.6886%;\"\u003e\n \u003cp\u003e2.42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 8.3045%;\"\u003e\n \u003cp\u003e10.30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 8.4775%;\"\u003e\n \u003cp\u003e12.73\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 9.3426%;\"\u003e\n \u003cp\u003e1.95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 9.6886%;\"\u003e\n \u003cp\u003e4.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 9.1696%;\"\u003e\n \u003cp\u003e4.47\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 6.7474%;\"\u003e\n \u003cp\u003e80. 96\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 6.7474%;\"\u003e\n \u003cp\u003e5.95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 7.4394%;\"\u003e\n \u003cp\u003e7.46\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 3.2872%;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 7.7855%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eDTM\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 6.5744%;\"\u003e\n \u003cp\u003e118\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 6.7474%;\"\u003e\n \u003cp\u003e127\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 9.6886%;\"\u003e\n \u003cp\u003e2.17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 8.3045%;\"\u003e\n \u003cp\u003e3.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 8.4775%;\"\u003e\n \u003cp\u003e5.26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 9.3426%;\"\u003e\n \u003cp\u003e1.21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 9.6886%;\"\u003e\n \u003cp\u003e1.45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 9.1696%;\"\u003e\n \u003cp\u003e1.88\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 6.7474%;\"\u003e\n \u003cp\u003e58. 86\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 6.7474%;\"\u003e\n \u003cp\u003e2.78\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 7.4394%;\"\u003e\n \u003cp\u003e2.28\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 3.2872%;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 7.7855%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eGFP\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 6.5744%;\"\u003e\n \u003cp\u003e34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 6.7474%;\"\u003e\n \u003cp\u003e47.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 9.6886%;\"\u003e\n \u003cp\u003e6.44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 8.3045%;\"\u003e\n \u003cp\u003e5.92\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 8.4775%;\"\u003e\n \u003cp\u003e5.43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 9.3426%;\"\u003e\n \u003cp\u003e11.35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 9.6886%;\"\u003e\n \u003cp\u003e5.55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 9.1696%;\"\u003e\n \u003cp\u003e8.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 6.7474%;\"\u003e\n \u003cp\u003e47.83\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 6.7474%;\"\u003e\n \u003cp\u003e3.32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 7.4394%;\"\u003e\n \u003cp\u003e7.91\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 3.2872%;\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 7.7855%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTSW\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 6.5744%;\"\u003e\n \u003cp\u003e19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 6.7474%;\"\u003e\n \u003cp\u003e45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 9.6886%;\"\u003e\n \u003cp\u003e17.39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 8.3045%;\"\u003e\n \u003cp\u003e16.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 8.4775%;\"\u003e\n \u003cp\u003e33.46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 9.3426%;\"\u003e\n \u003cp\u003e12.35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 9.6886%;\"\u003e\n \u003cp\u003e11.87\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 9.1696%;\"\u003e\n \u003cp\u003e17.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 6.7474%;\"\u003e\n \u003cp\u003e48.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 6.7474%;\"\u003e\n \u003cp\u003e5.72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 7.4394%;\"\u003e\n \u003cp\u003e16.94\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 3.2872%;\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 7.7855%;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePHT\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 6.5744%;\"\u003e\n \u003cp\u003e144.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 6.7474%;\"\u003e\n \u003cp\u003e283.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 9.6886%;\"\u003e\n \u003cp\u003e208.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 8.3045%;\"\u003e\n \u003cp\u003e821.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 8.4775%;\"\u003e\n \u003cp\u003e1030.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 9.3426%;\"\u003e\n \u003cp\u003e7.21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 9.6886%;\"\u003e\n \u003cp\u003e14.31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 9.1696%;\"\u003e\n \u003cp\u003e16.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 6.7474%;\"\u003e\n \u003cp\u003e79.75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 6.7474%;\"\u003e\n \u003cp\u003e52.74\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 7.4394%;\"\u003e\n \u003cp\u003e26.33\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 3.2872%;\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 7.7855%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSH\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 6.5744%;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 6.7474%;\"\u003e\n \u003cp\u003e38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 9.6886%;\"\u003e\n \u003cp\u003e26.92\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 8.3045%;\"\u003e\n \u003cp\u003e61.29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 8.4775%;\"\u003e\n \u003cp\u003e88.21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 9.3426%;\"\u003e\n \u003cp\u003e23.61\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 9.6886%;\"\u003e\n \u003cp\u003e35.62\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 9.1696%;\"\u003e\n \u003cp\u003e42.73\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 6.7474%;\"\u003e\n \u003cp\u003e69.48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 6.7474%;\"\u003e\n \u003cp\u003e13.44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 7.4394%;\"\u003e\n \u003cp\u003e61.16\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 3.2872%;\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 7.7855%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eLL\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 6.5744%;\"\u003e\n \u003cp\u003e60.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 6.7474%;\"\u003e\n \u003cp\u003e86.85\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 9.6886%;\"\u003e\n \u003cp\u003e13.33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 8.3045%;\"\u003e\n \u003cp\u003e23.54\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 8.4775%;\"\u003e\n \u003cp\u003e36.87\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 9.3426%;\"\u003e\n \u003cp\u003e4.89\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 9.6886%;\"\u003e\n \u003cp\u003e6.512\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 9.1696%;\"\u003e\n \u003cp\u003e8.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 6.7474%;\"\u003e\n \u003cp\u003e63.86\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 6.7474%;\"\u003e\n \u003cp\u003e7.99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 7.4394%;\"\u003e\n \u003cp\u003e10.72\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 3.2872%;\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 7.7855%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eLW\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 6.5744%;\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 6.7474%;\"\u003e\n \u003cp\u003e11.35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 9.6886%;\"\u003e\n \u003cp\u003e0.51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 8.3045%;\"\u003e\n \u003cp\u003e0.26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 8.4775%;\"\u003e\n \u003cp\u003e0.77\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 9.3426%;\"\u003e\n \u003cp\u003e7.61\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 9.6886%;\"\u003e\n \u003cp\u003e5.376\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 9.1696%;\"\u003e\n \u003cp\u003e9.36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 6.7474%;\"\u003e\n \u003cp\u003e32.97\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 6.7474%;\"\u003e\n \u003cp\u003e0.59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 7.4394%;\"\u003e\n \u003cp\u003e6.36\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 3.2872%;\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 7.7855%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eLA\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 6.5744%;\"\u003e\n \u003cp\u003e389.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 6.7474%;\"\u003e\n \u003cp\u003e657.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 9.6886%;\"\u003e\n \u003cp\u003e2732.98\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 8.3045%;\"\u003e\n \u003cp\u003e2391.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 8.4775%;\"\u003e\n \u003cp\u003e5124.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 9.3426%;\"\u003e\n \u003cp\u003e10.47\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 9.6886%;\"\u003e\n \u003cp\u003e9.79\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 9.1696%;\"\u003e\n \u003cp\u003e14.34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 6.7474%;\"\u003e\n \u003cp\u003e46.67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 6.7474%;\"\u003e\n \u003cp\u003e68.83\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 7.4394%;\"\u003e\n \u003cp\u003e13.79\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 3.2872%;\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 7.7855%;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePW\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 6.5744%;\"\u003e\n \u003cp\u003e6.65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 6.7474%;\"\u003e\n \u003cp\u003e11.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 9.6886%;\"\u003e\n \u003cp\u003e1.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 8.3045%;\"\u003e\n \u003cp\u003e0.42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 8.4775%;\"\u003e\n \u003cp\u003e1.58\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 9.3426%;\"\u003e\n \u003cp\u003e11.72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 9.6886%;\"\u003e\n \u003cp\u003e7.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 9.1696%;\"\u003e\n \u003cp\u003e13.68\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 6.7474%;\"\u003e\n \u003cp\u003e26.46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 6.7474%;\"\u003e\n \u003cp\u003e0.69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 7.4394%;\"\u003e\n \u003cp\u003e7.46\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 3.2872%;\"\u003e\n \u003cp\u003e11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 7.7855%;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePL\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 6.5744%;\"\u003e\n \u003cp\u003e19.85\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 6.7474%;\"\u003e\n \u003cp\u003e32.33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 9.6886%;\"\u003e\n \u003cp\u003e3.42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 8.3045%;\"\u003e\n \u003cp\u003e4.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 8.4775%;\"\u003e\n \u003cp\u003e7.55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 9.3426%;\"\u003e\n \u003cp\u003e7.45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 9.6886%;\"\u003e\n \u003cp\u003e8.19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 9.1696%;\"\u003e\n \u003cp\u003e11.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 6.7474%;\"\u003e\n \u003cp\u003e54.88\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 6.7474%;\"\u003e\n \u003cp\u003e3.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 7.4394%;\"\u003e\n \u003cp\u003e12.51\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 3.2872%;\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 7.7855%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eNHP\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 6.5744%;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 6.7474%;\"\u003e\n \u003cp\u003e35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 9.6886%;\"\u003e\n \u003cp\u003e29.51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 8.3045%;\"\u003e\n \u003cp\u003e63.37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 8.4775%;\"\u003e\n \u003cp\u003e92.87\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 9.3426%;\"\u003e\n \u003cp\u003e26.42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 9.6886%;\"\u003e\n \u003cp\u003e38.71\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 9.1696%;\"\u003e\n \u003cp\u003e46.87\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 6.7474%;\"\u003e\n \u003cp\u003e68.23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 6.7474%;\"\u003e\n \u003cp\u003e13.55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 7.4394%;\"\u003e\n \u003cp\u003e65.87\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 3.2872%;\"\u003e\n \u003cp\u003e13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 7.7855%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eNLPP\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 6.5744%;\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 6.7474%;\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 9.6886%;\"\u003e\n \u003cp\u003e0.30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 8.3045%;\"\u003e\n \u003cp\u003e0.99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 8.4775%;\"\u003e\n \u003cp\u003e1.29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 9.3426%;\"\u003e\n \u003cp\u003e5.55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 9.6886%;\"\u003e\n \u003cp\u003e10.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 9.1696%;\"\u003e\n \u003cp\u003e11.49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 6.7474%;\"\u003e\n \u003cp\u003e76.72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 6.7474%;\"\u003e\n \u003cp\u003e1.79\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 7.4394%;\"\u003e\n \u003cp\u003e18.17\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 3.2872%;\"\u003e\n \u003cp\u003e14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 7.7855%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eGY\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 6.5744%;\"\u003e\n \u003cp\u003e0.29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 6.7474%;\"\u003e\n \u003cp\u003e4.97\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 9.6886%;\"\u003e\n \u003cp\u003e0.17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 8.3045%;\"\u003e\n \u003cp\u003e1.48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 8.4775%;\"\u003e\n \u003cp\u003e1.66\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 9.3426%;\"\u003e\n \u003cp\u003e16.88\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 9.6886%;\"\u003e\n \u003cp\u003e49.75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 9.1696%;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;52.54\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 6.7474%;\"\u003e\n \u003cp\u003e89.67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 6.7474%;\"\u003e\n \u003cp\u003e2.38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 7.4394%;\"\u003e\n \u003cp\u003e97.04\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003eKey:\u0026nbsp;\u003c/strong\u003e \u003cem\u003eDTF=date of flowering, DTM= date of maturity, GFP=grain filling period, TSW=thousand seed weight, PHT=plant height, SH=stand at harvest, LL=leaf length, LW=leaf width, LA=leaf area, PW=panicle width, PL=panicle length, NHP=number of head per plot, NLPP=number of leaf per plant, GY= grain yield, GA= genetic advance, GAM= genetic advance as mean,H\u003csup\u003e2\u003c/sup\u003e=broad sense heritability, GCV=genotypic coefficient of variation, PCV= phenotypic coefficient of variation, ECV=environmental coefficient variation\u003c/em\u003e\u003c/p\u003e"},{"header":"Summary and Conclusions","content":"\u003cp\u003eUnderstanding the relationship and amount of genetic variability between yield and other agronomic characteristics is crucial in plant breeding programs, as it forms the foundation for effective selection. In study 72, sorghum elite genotypes were evaluated for their drought tolerance under stress conditions during the season of 2020 at the Miesso and Kobo site. Alpha lattice design with two replications was used to employ the experimental design. The analysis of variance showed significant differences among the genotypes for all the traits examined. Given that successful crop improvement requires heterogeneity within populations, the presence of significant genotype variability suggests the possibility of enhancing these qualities through both direct and indirect selection.\u003c/p\u003e\n\u003cp\u003eAccording to the present results of combined analysis from both locations, higher phenotypic over genotypic coefficient of variation were recorded for all traits with a range of \u0026nbsp;GCV 1.45% for number of days to maturity to 49.75 % for grain yield, PCV 1.88% for number of days to maturity to 52.54% for grain yield. Broad sense heritability ranges from 32.97% for leaf width to 89.67% for grain yield, with genetic progress as a percentage of the mean ranging from 2.28% for days to maturity to 97.04% for grain output. Grain yield, number of heads per plot, and stand count at harvest showed higher GCV along with higher heritability and GAM and thus such traits are controlled by additive gene action and could be improved through recurrent selection. However, the number of leaves per plant, leaf length, and days to 50% flowering had lower GAM and higher heritability, indicating that these traits are mostly governed by non-additive gene types and cannot be changed through selection.\u003c/p\u003e\n\u003cp\u003eThe current data indicated that the genotypes ETSC14804-4-2 (4.97 t/ha), ETSC14695-1-2 (4.7 t/ha), and ETSC14715-3-1 (4.46 t/ha) were high yielders compared to the other genotypes under investigation. \u0026nbsp;However, it is important to note that these results and conclusions are derived from data collected during a one-year field investigation at both locations. Thus, to get thorough results and draw reliable conclusions and suggestions, a greater number of accessions will be evaluated under moisture stress areas.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe would like to express our sincere gratitude to the Ethiopian Institute of Agricultural Research for their financial support throughout the study. Additionally, we extend our heartfelt thanks to the staff of the Melkassa Agricultural Research Center (National Sorghum Program) and the Chiro National Sorghum Research and Training Centers for their generosity in allowing us to use their facilities and for their support during the field experiments.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that there are no conflicts of interest.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAbraha, T., Githiri, S. M., Kasili, R., Araia, W. and Nyende, A. B. (2015). Genetic Variation among Sorghum [\u003cem\u003eSorghum bicolor\u003c/em\u003e \u003cem\u003eL. Moench\u003c/em\u003e] Landraces from Eritrea under Post Flowering Drought Stress Conditions. \u003cem\u003eAmerican Journal of Plant Sciences,\u003c/em\u003e 6(6): 1410- 1424. \u003c/li\u003e\n\u003cli\u003eAdane, G. \u003cem\u003eet al.\u003c/em\u003e (2018) \u0026lsquo;Genetic variability in agronomic traits and associations in sorghum [\u003cem\u003e(Sorghum bicolor (L.) Moench)\u003c/em\u003e] genotypes at intermediate agro-ecology sorghum growing areas of Ethiopia. \u003cem\u003eAfrican Journal of Agricultural Research\u003c/em\u003e, 13(49): 2780\u0026ndash;2787. \u003c/li\u003e\n\u003cli\u003eAl-Tabbal, J. A., \u0026amp; Al-Fraihat, A. H. (2012). Genetic variation, heritability, phenotypic and genotypic correlation studies for yield and yield components in promising barley genotypes. \u003cem\u003eJournal of Agricultural Science\u003c/em\u003e, \u003cem\u003e4\u003c/em\u003e(3), 193.\u003c/li\u003e\n\u003cli\u003eAmare, K., Zeleke, H. and Bultosa, G. 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Heterosis and inbreeding depression in rice. \u003cem\u003eMadrasAgric. J, \u003c/em\u003e60\u003cstrong\u003e, \u003c/strong\u003e1139-1140.\u003c/li\u003e\n\u003cli\u003eTesfaye, K. (2018) \u0026lsquo;Genetic diversity study of sorghum (\u003cem\u003eSorghum bicolor (L.) Moench\u003c/em\u003e) genotypes, Ethiopia\u0026rsquo;, \u003cem\u003eActa Universitatis Sapientiae, Agriculture and Environment\u003c/em\u003e, 9(1), 44\u0026ndash;54. \u003c/li\u003e\n\u003cli\u003eTeshome, A., Baum, B. R., Fahrig, L., Torrance, J. K., Arnason, T. J., Lambert, J. D. (1997), Sorghum [\u003cem\u003eSorghum bicolor (L.)\u003c/em\u003e Moench] landrace variation and classification in North Shewa and South Welo, Ethiopia. \u003cem\u003eEuphytica \u003c/em\u003e97, 255\u0026ndash;263.\u003c/li\u003e\n\u003cli\u003eYitayeh, Z. S., Mindaye, T. T., \u0026amp; Bisetegn, K. B. (2019). AMMI and GGE Analysis of G\u0026times; E and Yield Stability of Early Maturing Sorghum [\u003cem\u003eSorghum bicolor (L.) Moench\u003c/em\u003e] Genotypes in Dry Lowland Areas of Ethiopia. \u003cem\u003eAdvances in Crop Science and Technology\u003c/em\u003e, \u003cem\u003e5\u003c/em\u003e, 425.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Sorghum, Traits, Genotypes, Heritability, Genetic Variability","lastPublishedDoi":"10.21203/rs.3.rs-6178149/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6178149/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"In dry lowland regions of the world, sorghum (Sorghum bicolor (L) Moench) is a significant crop extensively farmed for food, feed, fodder, and fuel. Despite extensive breeding work, sorghum production and productivity remain low in Ethiopia. To create superior genotypes in breeding programs, it is necessary to comprehend the existence and extent of genetic diversity. Therefore, this research aimed to evaluate the importance of genetic diversity, heritability, and genetic progress among the genotypes of sorghum. Using an alpha lattice design with two replications, a total of 72 genotypes were assessed during the main cropping season of 2020 in Miesso in Eastern Ethiopia and Kobo in Northern Ethiopia. For every trait, a combined analysis of variance revealed a significant difference (p\u003c0.01) between the genotypes. Grain yield, the number of heads per plot, and the number of stands at harvest had the highest genotypic and phenotypic coefficients of variation, while the number of days to flowering, days to maturity, grain filling period, leaf length, and leaf width had the lowest. Genetic advance as a percentage of the mean (GAM) varied from 2.28% for the number of days to maturity to 97.04% for grain yield, while broad sense heritability ranged from 26.46% for panicle width to 89.67% for grain yield. The genotypes ETSC14804-4-2 (4.97 t/ha), ETSC14695-1-2 (4.7 t/ha), and ETSC14715-3-1 (4.46 t/ha) were found to be high-yielders in comparison to the others based on the current data. Still, more research is required to make better recommendations.","manuscriptTitle":"Assessment of genetic variation, heritability, and genetic advance among elite sorghum [Sorghum bicolor (L) Moench] lines for yield and yield associated traits under moisture stress areas","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-08-28 03:53:20","doi":"10.21203/rs.3.rs-6178149/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"e14e0f55-5aa8-42c6-9216-0eb3b2e8a2af","owner":[],"postedDate":"August 28th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-08-28T03:53:20+00:00","versionOfRecord":[],"versionCreatedAt":"2025-08-28 03:53:20","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6178149","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6178149","identity":"rs-6178149","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}